RNA-Seq: differential expression analysis of controls
Libraries required
Filtering gene counts
Function
Differeitial analysis using limma
SVA
se <- SummarizedExperiment(assays = counts.filt)
se@colData <- DataFrame(salmon@phenoData)
sv.pl <- svacor(SE = se, form = ~ 0 + Group)## Using variance-stabilizing transformation## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## Number of significant surrogate variables is: 4
## Iteration (out of 5 ):1 2 3 4 5limma fit
design <- model.matrix(~ 0 + SV1 + SV2 + SV3 + Group, data = sv.pl@colData)
colnames(design) <- gsub(pattern = "Group", replacement = "", x = colnames(design))
dds <- calcNormFactors(DGEList(counts = counts.filt))
v <- voom(dds, design = design)
contrast.matrix <- makeContrasts(PND15 - PND8, Adult - PND15, levels = design)
fit <- lmFit(v)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)DEA results
pnd8.pnd15 <- as.data.frame(topTable(fit2, coef = 1, number = Inf))
pnd8.pnd15 <- data.frame(Genes = rownames(pnd8.pnd15), pnd8.pnd15, stringsAsFactors = F)
pnd15.adult <- as.data.frame(topTable(fit2, coef = 2, number = Inf))
pnd15.adult <- data.frame(Genes = rownames(pnd15.adult), pnd15.adult, stringsAsFactors = F)
vst Counts data and pData
dge <- DGEList(counts = counts.filt, group = salmon@phenoData$Group)
dge <- calcNormFactors(object = dge, method = "TMM")
cpm <- cpm(dge, log = T)
cpm <- removeBatchEffect(x = cpm, covariates = design[, 1:3], design = design[, 4:6])
data <- list(cpm = cpm, vstSV = assay(sv.pl), voomE = v$E, pData = salmon@phenoData)
save(data,
file = "./output/data_pData_tx.RData", compress = T,
compression_level = 3
)Results
dea.list <- list(
`PND8 vs PND15` = as.DEA(pnd8.pnd15),
`PND15 vs Adult` = as.DEA(pnd15.adult)
)
normCounts <- v$E
voomEList <- v
dea.limma <- list(
`PND8 vs PND15` = pnd8.pnd15,
`PND15 vs Adult` = pnd15.adult
)Save RData files
save(dea.list,
file = "./output/tx_dea_SC_Controls.DEA.RData", compress = T,
compression_level = 3
)
save(normCounts,
file = "./output/tx_normCounts_voom_SC_Controls.RData", compress = T,
compression_level = 3
)
save(voomEList,
file = "./output/tx_voom_EList_SC_Controls.RData", compress = T,
compression_level = 3
)
save(dea.limma,
file = "./output/tx_limma_SC_Controls.RData", compress = T,
compression_level = 3
)PCA plots
Raw counts filtered
SVA (VST)
SessionInfo
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.2 (2019-12-12)
os Ubuntu 16.04.6 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Zurich
date 2020-07-08
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib
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annotate 1.64.0 2019-10-29 [1]
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SRAdb 1.48.2 2019-12-24 [1]
stringi 1.4.6 2020-02-17 [1]
stringr 1.4.0 2019-02-10 [1]
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survival 3.1-12 2020-04-10 [1]
sva * 3.34.0 2019-10-29 [1]
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